What if airports could cut delays by 50% without adding a single gate?

As the aviation industry faces rising passenger volumes, operational complexity and sustainability pressures, the need for intelligent, real-time decision-making across physical environments has never been greater.

Physical AI brings together AI, edge computing and IoT to help systems perceive, predict and support autonomous action in dynamic, high-mobility environments such as airports and airline operations.

At HCLTech, we are helping aviation stakeholders explore how Physical AI can improve efficiency, safety and scalability across complex operating environments. This article examines key challenges in airport and airline operations and how Physical AI can support more intelligent, connected and resilient ways of working.

Aviation under pressure: Complexity in motion

The aviation industry continues to operate under significant pressure despite record passenger demand and resilient profitability. IATA’s 2026 outlook projects 5.2 billion passengers in 2026, industry revenues of $1.053 trillion and a net profit margin of 3.9%. The same outlook points to persistent supply chain constraints, infrastructure pressures, rising non-fuel costs and climbing maintenance costs linked to aging fleets.

These pressures make operational efficiency, resilience and real-time decision-making critical priorities.

From detection to decisions: The closed-loop Physical AI advantage

Airports are dynamic, safety-critical environments where small deviations, such as an emerging queue, a delayed turnaround step or a baggage belt slowdown, can cascade into wider disruption.

What aviation leaders increasingly need is not just monitoring, but a closed-loop intelligence layer that detects meaningful change, understands impact in context and triggers the right response.

The Physical AI approach connects real-time perception with a living operational digital twin. At the core is a “physical reality to digital twin change detection” loop: capturing real-world signals from cameras, sensors, LiDAR, telemetry and PLC data, mapping them to the twin, detecting deviations or state changes and triggering updates, alerts, predictions or control actions.

This closed loop is especially valuable in airports because operations span multiple interconnected domains, including terminal flow, security, baggage, apron movement and facility systems.

Flight delays and congestion

Flight delays and congestion remain persistent challenges as passenger volumes grow faster than physical infrastructure and network capacity. The most durable pressure points are recurring constraints: severe weather, airspace congestion, constrained aircraft availability, crew and ground-resource limitations, air traffic control modernization gaps and hub complexity.

In this environment, small operational deviations, such as late inbound aircraft, slower boarding, gate conflicts or security queue build-up, can quickly cascade across the network.

To address these challenges, HCLTech’s VisionX platform transforms existing camera infrastructure into intelligent monitoring systems. By processing live video feeds at the edge, VisionX can detect congestion, predict bottlenecks, quantify crowding and queue build-up and support faster operational response.

This helps airports improve throughput at checkpoints, boarding gates and passenger-processing areas without relying solely on costly physical expansion.

Turnaround inefficiencies

While congestion is a visible pain point, inefficiencies behind the scenes, such as aircraft turnaround, compound the problem.

Aircraft turnaround remains one of the most complex processes in aviation. Assaia’s 2025 Turnaround Benchmark Report analyzed more than 450,000 turns and found a 25% reduction in median departure delays, with median departure delays reduced from four to three minutes. It also found a 5% increase in turns per stand, showing how better-managed turnarounds can help airports free up capacity without new infrastructure and improve punctuality.

With airport expansion constrained and aircraft deliveries uncertain, optimizing turnaround has become a critical operational lever.

HCLTech’s SmarTwin solution creates AI-enhanced digital replicas of airport assets and workflows, allowing operators to simulate scenarios, optimize layouts and coordinate ground operations with precision.

SmarTwin is built on an open architecture powered by NVIDIA Omniverse and OpenUSD, enabling interoperability across engineering systems, operational technologies, IoT platforms and AI applications. This allows airports and airlines to evolve their digital twin investments without being locked into a single technology stack, while supporting high-fidelity simulation, reuse of digital assets and faster integration with third-party engineering tools., enabling interoperability across engineering systems, operational technologies, IoT platforms and AI applications. This allows airports and airlines to evolve their digital twin investments without being locked into a single technology stack, while supporting high-fidelity simulation, reuse of digital assets and faster integration with third-party engineering tools.

In turnaround operations, this translates into scenario-based coordination, including what-if simulations, constraint mapping and workflow optimization across fueling, catering, cleaning, baggage, boarding and pushback readiness.

Rather than relying on a single generic savings figure, the value case should be framed around measurable operational outcomes: fewer avoidable delay minutes, improved gate utilization, better schedule recovery, reduced manual coordination effort and more consistent on-time performance through better decision confidence and orchestration.

Labor constraints

Workforce pressure remains a structural challenge across aviation, but it is more accurate to describe it as a long-term workforce sustainability issue rather than a single near-term shortage number.

Boeing’s 2025-2044 Pilot and Technician Outlook projects that the global commercial aviation industry will need 660,000 new pilots, 710,000 new maintenance technicians and one million new cabin crew members through 2044 to fly and maintain the global commercial aviation fleet over the next 20 years.

Physical AI does not replace licensed aviation professionals. Its role is to help airlines and airports operate more resiliently when skilled labor is constrained.

HCLTech’s Physical AI stack combines VisionX and SmarTwin to support automation, event validation, resource planning and faster operational response. This helps teams focus limited human capacity on higher-value decisions while improving continuity across terminal, gate, ramp, baggage and maintenance operations.

Baggage mishandling

Every mishandled bag is both an operational cost and a passenger-trust issue.

According to SITA’s 2026 Baggage IT Insights report, baggage performance improved materially in 2025: the global mishandling rate fell 23% to 4.9 bags per 1,000 passengers and total mishandled bags declined to approximately 24 million. The issue remains significant, with mishandled baggage still costing the industry an estimated $6.3 billion annually with each bag carrying an average cost of $260.

As passenger volumes continue to rise, baggage resilience increasingly depends on connected data, real-time tracking, AI-assisted routing and faster exception management.

HCLTech’s Physical AI can help by linking track-and-trace signals with operational context, so exceptions are detected earlier, routed faster and resolved more consistently. In an airport ecosystem, SmarTwin’s airport use-case blueprint includes a self-healing baggage handling system, reinforcing how a living twin can support faster detection and recovery from baggage handling system disruptions.

This can be complemented by HCLTech’s TraceX solution, which leverages IoT and AI to monitor baggage and ground equipment in real time.

The value case should be framed around reducing mishandling, shortening recovery time, lowering rehandling effort and improving passenger confidence rather than relying on a single universal reduction percentage.

Aging fleets and maintenance costs

Aircraft delivery delays, engine reliability issues and supply chain constraints are forcing airlines to keep older aircraft in service longer than planned.

Oliver Wyman’s 2026-2036 Global Fleet and MRO Market Forecast says the industry began 2026 with about 17,000 unfilled aircraft orders, a backlog expected to take more than 12 years to clear at current production rates. As aircraft accumulate more flight hours and remain in service longer, airlines need better tools to anticipate failures, prioritize maintenance actions and reduce operational disruption.

HCLTech's VisionX platform can detect operational anomalies in real time and trigger automated alerts and workflows, helping airlines improve asset reliability and respond faster to maintenance issues.

Autonomous ground support orchestration

Deploying autonomous vehicles, robots and AI-assisted ground equipment across terminals and ramp areas requires more than navigation. It requires coordinated operational intelligence.

Airports are increasingly exploring autonomous baggage carts, ramp robotics, cleaning robots, AI-assisted vehicle routing and integrated airport operations centers to improve throughput and reduce manual coordination burden.

HCLTech integrates VisionX, SmarTwin and TraceX to support safe navigation, route optimization, asset visibility and proactive conflict avoidance across mixed human-machine environments.

This approach connects multimodal edge perception, movement visibility and a living operational twin so airports can continuously sense reality, understand operational context, simulate responses and coordinate action at speed.

The result is a closed-loop operating model that can support safer autonomous movement, faster incident response and more consistent orchestration across gate, ramp, baggage and terminal domains.

Together, VisionX, SmarTwin and TraceX create a physical-reality-to-digital-twin loop that detects drift, validates events, simulates the best response and supports safe autonomous operations in real time.

Real-world impact: Physical AI in action

In large-scale logistics and high-mobility operating environments, the same Physical AI principles can help improve resilience and efficiency when AI-driven monitoring, predictive analytics and automated workflows are integrated into daily operations.

Where customer details cannot be publicly disclosed, the example should be positioned as an illustrative implementation outcome rather than a named public case study.

Representative outcomes may include:

• Up to 40% reduction in unplanned downtime through predictive maintenance and AI-driven monitoring
• Up to 25% improvement in logistics cost efficiency through workflow digitalization, automation and real-time operational visibility
• Improved compliance monitoring and audit readiness through automation, continuous controls monitoring and faster evidence collection

These types of improvements can streamline operations, improve asset utilization and support sustainability by reducing avoidable downtime, energy waste and rework.

The example demonstrates how the same Physical AI principles can apply across complex, high-mobility environments beyond aviation, while the aviation-specific value case should be validated against each airport or airline’s operational baseline.

Autonomous airports and sustainability goals

Over the rest of this decade, Physical AI is likely to become an increasingly important enabler of more autonomous airport operations.

By connecting robotics, AI-driven decision-making, predictive maintenance and operational digital twins, airports and airlines can improve operational efficiency while supporting sustainability goals by reducing carbon emissions, optimizing energy usage and minimizing waste.